Summary of Caesar: a Low-deviation Compression Approach For Efficient Federated Learning, by Jiaming Yan et al.
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
by Jiaming Yan, Jianchun Liu, Hongli Xu, Liusheng Huang, Jiantao Gong, Xudong Liu, Kun Hou
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel federated learning (FL) framework called Caesar is proposed to alleviate the communication overhead in FL systems by striking a balance between model accuracy and traffic cost. The framework employs low-deviation compression for global model downloads and local gradient uploads, taking into account staleness of local models, sample volume, and label distribution. Additionally, fine-grained batch size optimization reduces idle waiting time under synchronized barriers. Experimental results on two physical platforms with 40 smartphones and 80 NVIDIA Jetson devices show that Caesar can reduce traffic costs by up to 37.88% while maintaining comparable final test accuracy compared to full-precision communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Caesar is a new way to learn from many different devices without sharing all the data. It helps reduce the amount of information sent between devices, which makes it more efficient and saves time. The approach uses special techniques for compressing data and optimizing how much data is shared. This allows devices to learn faster and use less energy while keeping the quality of the results good. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Precision
